Stationary Equilibria in Behavioral Game Theory: An Experimental Analysis of Inspection Games

37 Pages Posted: 9 Apr 2024

See all articles by Vinícius Ferraz

Vinícius Ferraz

Heidelberg University

Thomas Pitz

Rhine-Waal University of Applied Sciences

Wolf Gardian

affiliation not provided to SSRN

Deniz Kayar

affiliation not provided to SSRN

Jörn Sickmann

Rhine-Waal University of Applied Sciences

Abstract

This paper investigates the predictive capabilities of different stationary equilibrium concepts within the framework of an inspector game. The experiment employed a 2x2 asymmetric payoff design spanning 70 periods, executed under information-loaded and neutral frames. Drawing on data from 100 participants, we analyze five established stationary equilibria concepts and five modified versions incorporating loss aversion and fairness parameters. Our analysis emphasizes predictive performance and model characteristics on aggregated, time series, and game-play data aggregations. The results show the limited predictive power of the Nash Equilibrium, while the Action Sampling Equilibrium and Impulse Balance Equilibrium emerge as the best predictors among the original concepts. The modified models exhibit high predictive potential but also increased calculation complexity and parameter estimation. The modified Impulse Balance Equilibrium with a dynamic loss aversion parameter stands out for its predictive power and robust representation of the loss-aversion behavioral trait.

Keywords: Behavioral Game theory, Equilibrium Theory, Stationary Equilibrium Concepts, Inspection Games, Competition

Suggested Citation

Ferraz, Vinícius and Pitz, Thomas and Gardian, Wolf and Kayar, Deniz and Sickmann, Jörn, Stationary Equilibria in Behavioral Game Theory: An Experimental Analysis of Inspection Games. Available at SSRN: https://ssrn.com/abstract=4788713 or http://dx.doi.org/10.2139/ssrn.4788713

Vinícius Ferraz (Contact Author)

Heidelberg University ( email )

Grabengasse 1
Heidelberg, 69117
Germany

Thomas Pitz

Rhine-Waal University of Applied Sciences ( email )

Wolf Gardian

affiliation not provided to SSRN

Deniz Kayar

affiliation not provided to SSRN ( email )

No Address Available

Jörn Sickmann

Rhine-Waal University of Applied Sciences ( email )

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